We may earn an affiliate commission when you visit our partners.

Classification Trees in Python, From Start To Finish

In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.

Prerequisites:

In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices.

Notes:

- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops foundational Decision Trees skills, which are highly relevant to AI and Data Science

Builds foundation for more advanced courses and training in AI and Data Science

Teaches continuous and categorical data handling, which is standard in industry

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Practical classification trees in python

According to learners, this course offers a highly practical, project-based approach to building Classification Trees in Python. Students particularly praise the seamless setup of the Rhyme platform, which provides pre-configured cloud desktops, allowing them to dive directly into hands-on coding without environmental hurdles. While many find the 1-hour duration efficient for targeted learning, some note that the course assumes significant prior knowledge of Python and machine learning theory, making it less suitable for absolute beginners. Additionally, the regional restriction to North America for optimal experience is a notable consideration for prospective students outside this area.

Delivers targeted knowledge within a short timeframe.

"Perfect for a quick refresh on classification trees and pruning techniques using a real-world scenario."

"In just one hour, I got exactly what I needed to understand the practical steps of building the tree."

"The course is very direct and doesn't waste time, which I value as a busy professional looking for specific skills."

The Rhyme platform makes getting started effortless.

"Loved that I didn't have to worry about setting up Python or Jupyter; it was all ready to go instantly."

"Instant access to a pre-configured cloud desktop saved me so much time on environment configuration."

"The pre-installed tools meant I could focus entirely on the learning and the project, not technical issues."

Excellent for applying concepts with coding projects.

"The project-based format is really helpful for seeing how classification trees work in practice."

"I appreciate the practical application of concepts, not just theory, with the real-world dataset."

"Actually coding along with the instructor made a huge difference in my understanding of the process."

Only fully supported in North America, impacting others.

"As a learner outside North America, I experienced some technical issues with the Rhyme environment's performance."

"Wish this course was more accessible globally; the regional restriction is a significant drawback for international students."

"I hope they expand support for the Rhyme platform to other regions soon to improve access for everyone."

One hour limits how deep concepts can be explored.

"While titled 'From Start To Finish', it's a very high-level overview, not a deep dive into every nuance of the methods."

"I felt it covered the steps from start to finish for this specific example, but not generalizable advanced scenarios."

"It's a great introduction for applying these techniques, but don't expect comprehensive theoretical coverage; it's brief."

Requires solid understanding of Python and ML theory.

"As someone new to ML, I found myself struggling; it really assumes you know the basics of decision trees and pruning already."

"The course moves quickly if you're not already familiar with cost complexity pruning and cross-validation techniques."

"I was glad I already knew Python; otherwise, this course would have been too advanced and fast-paced for me to follow."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Classification Trees in Python, From Start To Finish with these activities:

Brush Up on Python Basics

Show steps

Ensure a strong foundation in Python syntax and data structures to support your learning in this course.

Browse courses on Python

Show steps

Review basic Python concepts, such as variables, data types, and control flow
Practice writing simple Python scripts and functions

Read 'Python Machine Learning' by Sebastian Raschka

Show steps

Gain a comprehensive understanding of machine learning concepts and techniques, including classification trees, in the context of Python.

Show steps

Read the chapters on classification trees, focusing on the theory and implementation in Python
Work through the exercises and examples provided in the book

Compile Resources on Classification Trees

Show steps

Gather and organize valuable resources related to classification trees, providing easy access to additional learning materials.

Browse courses on Classification Trees

Show steps

Search for and identify reputable articles, tutorials, and online courses on classification trees
Create a central location, such as a digital folder or online document, to store the resources
Categorize and organize the resources for easy retrieval

Five other activities

Expand to see all activities and additional details

Show all eight activities

Solve Coding Challenges on Classification Trees

Show steps

Challenge yourself with coding problems related to classification trees, solidifying your understanding of their implementation and applications.

Browse courses on Classification Trees

Show steps

Find online coding platforms or challenges that offer classification tree problems
Attempt to solve the problems using Python, focusing on efficient and accurate solutions
Review your solutions and identify areas for improvement

Join a Study Group or Discussion Forum

Show steps

Engage with peers to discuss concepts, share insights, and reinforce your understanding of classification trees and Python.

Browse courses on Classification Trees

Show steps

Identify or join an online or in-person study group or discussion forum
Regularly participate in discussions, ask questions, and share your knowledge

Follow Tutorials on Advanced Classification Tree Techniques

Show steps

Explore advanced classification tree techniques, such as ensemble methods, to improve the accuracy and robustness of your models.

Browse courses on Classification Trees

Show steps

Identify online tutorials or courses covering advanced classification tree techniques
Follow the tutorials step-by-step, implementing the techniques in Python
Apply the learned techniques to your own classification tree model and evaluate the improvements

Create a Classification Tree Model from Scratch

Show steps

Build a classification tree from scratch to reinforce your understanding of the process and algorithms involved.

Browse courses on Classification Trees

Show steps

Gather a dataset with categorical and continuous values
Write code to pre-process the data, including handling missing values and encoding categorical data
Implement the Classification Tree algorithm using Python, including splitting criteria, node construction, and pruning

Design a Dashboard to Visualize Classification Tree Results

Show steps

Create a visually appealing dashboard to present the results of your classification tree model, making it easier to interpret and communicate.

Browse courses on Data Visualization

Show steps

Choose a data visualization tool or library, such as Plotly or Seaborn
Design the layout of the dashboard, including graphs, charts, and other visualizations
Develop code to connect the dashboard to your classification tree model and display the results

Career center

Learners who complete Classification Trees in Python, From Start To Finish will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are heavily reliant on classification trees to create and test predictive models. This course provides a solid foundation for building classification trees in Python, which is a crucial skill for Data Scientists. By understanding how to construct and optimize classification trees, learners can enhance their ability to analyze data, identify patterns, and make accurate predictions. Furthermore, this course covers topics such as cost complexity pruning and cross-validation, which are essential techniques for preventing overfitting and ensuring the robustness of models.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers play a critical role in developing and deploying machine learning models. This course provides a comprehensive introduction to building classification trees, which are a fundamental type of machine learning model. Learners will gain hands-on experience in implementing classification trees in Python, including techniques for handling missing data and categorical variables. By mastering these skills, learners can contribute effectively to the design, implementation, and maintenance of machine learning systems.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts leverage classification trees to uncover insights from data and make informed decisions. This course equips learners with the practical skills to build and interpret classification trees in Python. By learning how to prepare data, construct models, and evaluate their performance, learners can enhance their ability to analyze data, identify trends, and communicate findings effectively.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians often utilize classification trees to analyze complex data and make predictions. This course provides a practical foundation for building classification trees in Python, covering topics such as data preprocessing, model selection, and performance evaluation. By mastering these skills, learners can strengthen their ability to conduct statistical analyses, interpret results, and draw meaningful conclusions from data.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts leverage data analysis to drive informed decision-making. This course provides a valuable introduction to building classification trees in Python, a technique commonly used for data classification and prediction. Learners will gain hands-on experience in preparing data, constructing models, and interpreting results. By mastering these skills, learners can enhance their ability to analyze business data, identify opportunities, and make data-driven recommendations.

See salaries and explore the career path for Business Analyst

Research Analyst

Research Analysts utilize classification trees to analyze data, identify patterns, and make predictions. This course provides a comprehensive introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to conduct research, analyze data, and draw meaningful conclusions.

See salaries and explore the career path for Research Analyst

Quantitative Analyst

Quantitative Analysts rely on classification trees to build predictive models and analyze financial data. This course provides a solid foundation for building classification trees in Python, covering techniques for handling missing data, transforming categorical variables, and optimizing model performance. By mastering these skills, learners can enhance their ability to develop and evaluate quantitative models, identify investment opportunities, and make informed financial decisions.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

Risk Analysts leverage classification trees to assess and manage risk. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify risks, and develop mitigation strategies.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries utilize classification trees to analyze data and make predictions in the insurance industry. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, assess risk, and develop insurance products and pricing strategies.

See salaries and explore the career path for Actuary

Software Engineer

Software Engineers may encounter classification trees in the context of data analysis and machine learning tasks. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop data-driven applications, and make informed decisions.

See salaries and explore the career path for Software Engineer

Operations Research Analyst

Operations Research Analysts utilize classification trees to solve complex decision-making problems. This course provides a comprehensive introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop optimization models, and make informed decisions.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts may leverage classification trees to analyze financial data and make predictions. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify investment opportunities, and make informed financial decisions.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers may encounter classification trees in the context of data analysis and machine learning tasks. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, develop data pipelines, and ensure the integrity and quality of data.

See salaries and explore the career path for Data Engineer

Product Manager

Product Managers may leverage classification trees to analyze data and make decisions about product development. This course provides a practical introduction to building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify customer needs, and develop successful products.

See salaries and explore the career path for Product Manager

Business Development Manager

Business Development Managers may leverage classification trees to analyze data and identify sales opportunities. This course provides a solid foundation for building classification trees in Python, covering topics such as data preparation, model selection, and performance evaluation. By mastering these skills, learners can enhance their ability to analyze data, identify potential customers, and develop effective sales strategies.

See salaries and explore the career path for Business Development Manager

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Classification Trees in Python, From Start To Finish.

The Elements of Statistical Learning

Save

Comprehensive reference on statistical learning methods, including decision trees. It provides a theoretical and practical understanding of these algorithms.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

Provides a practical guide to building machine learning models in Python using popular libraries like scikit-learn and TensorFlow. It covers decision trees and other classification algorithms in-depth.

Classification Trees in Python, From Start To Finish

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical classification trees in python

Activities

Career center

Reading list

Share

Similar courses